Methods and apparatus to determine a state of a media presentation device

ABSTRACT

Methods and apparatus to determine a state of a media presentation device are disclosed. Example disclosed methods include generating a first set of weighted coefficients based on first audio received by first and second microphones at a first time. Example disclosed methods include generating a second set of weighted coefficients based on second audio received by the first and second microphones at a second time after the first time. Example disclosed methods include comparing the first set of coefficients and the second set of coefficients to generate a similarity value. Example disclosed methods include, when the similarity value satisfies a threshold, determining that the media presentation device is in a first state. Example disclosed methods include, when the similarity value does not satisfy the threshold, determining that the media presentation device is in a second state.

RELATED APPLICATION

This patent claims the benefit of U.S. Non Provisional application Ser.No. 15/925,360, entitled “Methods and Apparatus to Determine a State ofa Media Presentation Device,” which was filed on Mar. 19, 2018, whichclaims priority to U.S. Non-Provisional application Ser. No. 14/926,885,entitled “Methods and Apparatus to Determine a State of a MediaPresentation Device,” which was filed on Oct. 29, 2015, which claims thebenefit of U.S. Provisional Application Ser. No. 62/142,771, entitled“Methods and Apparatus to Determine a State of a Media PresentationDevice,” which was filed on Apr. 3, 2015. These applications are herebyincorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to market research, and, moreparticularly, to methods and apparatus to determine a state of a mediapresentation device.

BACKGROUND

Audience measurement systems may be used to identify content output by amedia presentation device. For example, a metering device can beequipped with microphone(s) to identify program content emanating from amedia presentation device, such as a television (TV). An audio signalcaptured by the microphone(s) is processed either to extract an embeddedwatermark from the audio signal or convert the audio signal to asignature for matching against signatures stored in a referencedatabase. Audio watermarks are embedded in media program content priorto distribution of the media program content for consumption. Referenceaudio signature databases are created from broadcast or distributedmedia program content.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system including a meter to determine a stateof a media presentation device.

FIG. 2 shows an example implementation of a metering device such as themeter of FIG. 1.

FIG. 3 illustrates an example implementation of a source detector suchas the source detector of FIG. 2.

FIG. 4 depicts an example filter weight distribution corresponding tofilter weights that indicate a monitored media presentation device isturned on.

FIG. 5 depicts another example filter weight distribution correspondingto filter weights that indicate a monitored media presentation device isturned off.

FIG. 6 depicts an example filter apparatus implementing an adaptiveleast mean square algorithm using a finite impulse response (FIR)filter, such as in the example source detector of FIG. 3.

FIG. 7 depicts an example implementation of a state determiner such asthe state determiner of FIG. 3.

FIG. 8 is a flow diagram representative of example machine readableinstructions that may be executed to implement a monitoring and audiencemeasurement process including an example metering device and its sourcedetector of FIGS. 1-3 and 6-7.

FIG. 9 is a flow diagram providing additional detail representative ofexample machine readable instructions that may be executed to implementa portion of the flow diagram of FIG. 8.

FIG. 10 is a flow diagram providing additional detail representative ofexample machine readable instructions that may be executed to implementa portion of the flow diagram of FIG. 8.

FIG. 11 is a block diagram of an example processor system structured toexecute the example machine readable instructions represented by FIGS.8-10 to implement the example metering device and its source detector ofFIGS. 1-3 and 6-7.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific examples that may be practiced. Theseexamples are described in sufficient detail to enable one skilled in theart to practice the subject matter, and it is to be understood thatother examples may be utilized and that logical, mechanical, electricaland other changes may be made without departing from the scope of thesubject matter of this disclosure. The following detailed descriptionis, therefore, provided to describe example implementations and not tobe taken as limiting on the scope of the subject matter described inthis disclosure. Certain features from different aspects of thefollowing description may be combined to form yet new aspects of thesubject matter discussed below.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” “the,” and “said” are intended tomean that there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

In many microphone-based audience measurement environments, it isnecessary to determine a source of audio signals being captured by ametering, monitoring, or measurement device (referred to herein as a“metering device or a meter” for illustrative brevity). If a source ofcaptured audio signal is not properly identified, error is introducedinto the audience measurement data that is generated based on thecaptured audio.

However, in some environments, a monitoring or metering device capturesaudio not only emanating from a media presentation device of interest,but also from other sources including ambient noise, speech signals fromviewers talking to each other, etc. As disclosed herein, a source of thecaptured audio is identified to avoid erroneous audience measurementstemming from audio that is not emanating from the media presentationdevice of interest.

Examples disclosed herein facilitate audience measurement to determinewhether audio being captured is emanating from the media presentationdevice of interest. When watermarks embedded in media are notsuccessfully extracted, signatures computed from the microphone-capturedaudio may or may not represent the audio emanating from the mediapresentation device of interest. For example, the media presentationdevice of interest may be turned down or off, and the source of audiocaptured by the microphone can be any one of a plurality of otherpossible sources (e.g., people talking, other equipment in the room,ambient noise, etc.). In some such cases, it is possible to obtain falsematches for program signature information when monitoring signaturesgenerated by a metering or monitoring device are compared against alarge number of reference signatures in a reference database. Certainexamples confirm the media presentation device as source by analyzingthe audio signals captured by the metering device.

Examples disclosed herein facilitate reducing instances of falsematching by distinguishing audio emanating from the media presentationdevice of interest from audio generated by other potential sources. Forexample, disclosed examples facilitate identifying the mediapresentation device of interest as the source of the detected audio byanalyzing the audio captured by the metering device to determine whetherthe media presentation device of interest is 1) turned on or 2) turneddown or off.

Certain examples determine whether the media presentation device ofinterest is 1) turned on or 2) turned down or off by determining whetheror not the detected audio matches a characteristic of audio previouslydetected from the media presentation device. If the detected audiomatches a characteristic of audio previously detected from the mediapresentation device, then the media presentation device is inferred tobe turned on. However, if the detected audio does not match acharacteristic of audio previously detected from the media presentationdevice, then the media presentation device is inferred to be turned downor turned off.

When metering devices incorrectly credit exposure minutes, one or morehousehold tuning estimates and/or projections may be over-reportedand/or otherwise inflated. Additionally, attempted detection ofwatermarks and/or other codes by metering devices can return falsepositives and/or erroneous data if ambient noise interferes with propercapture and analysis of audio information from a media presentationdevice of interest. Example methods, apparatus, systems and/or articlesof manufacture disclosed herein distinguish instances of ambient soundto be ignored from instances of audio from a media presentation deviceof interest that are to be monitored.

Examples disclosed herein provide methods of determining a state of amedia presentation device. Disclosed example methods include generatinga first set of weighted coefficients based on first audio received byfirst and second microphones at a first time. Disclosed example methodsinclude generating a second set of weighted coefficients based on secondaudio received by the first and second microphones at a second timeafter the first time. Disclosed example methods include comparing thefirst set of coefficients and the second set of coefficients to generatea similarity value. Disclosed example methods include, when thesimilarity value satisfies a first threshold, determining that the mediapresentation device is in a first state. Disclosed example methodsinclude, when the similarity value does not satisfy the first threshold,determining that the media presentation device is in a second state.Disclosed example methods include controlling a metering device based onthe state of the media presentation device.

Examples disclosed herein provide tangible computer readable storagemedia having instruction that, when executed, cause a machine togenerate a first set of weighted coefficients based on first audioreceived by first and second microphones at a first time. Disclosedexample computer readable storage media have instructions that, whenexecuted, further cause the machine to generate a second set of weightedcoefficients based on second audio received by the first and secondmicrophones at a second time after the first time. Disclosed examplecomputer readable storage media have instructions that, when executed,further cause the machine to compare the first set of coefficients andthe second set of coefficients to generate a similarity value. Disclosedexample computer readable storage media have instructions that, whenexecuted, further cause the machine to, when the similarity valuesatisfies a first threshold, determine that the media presentationdevice is in a first state. Disclosed example computer readable storagemedia have instructions that, when executed, further cause the machineto, when the similarity value does not satisfy the first threshold,determine that the media presentation device is in a second state.Disclosed example computer readable storage media have instructionsthat, when executed, further cause the machine to control a meteringdevice based on the state of the media presentation device.

Examples disclosed herein provide apparatus including a metering deviceincluding a programmed processor. Disclosed example apparatus includethe processor programmed to generate a first set of weightedcoefficients based on first audio received by first and secondmicrophones at a first time. Disclosed example apparatus include theprocessor programmed to generate a second set of weighted coefficientsbased on second audio received by the first and second microphones at asecond time after the first time. Disclosed example apparatus includethe processor programmed to compare the first set of coefficients andthe second set of coefficients to generate a similarity value. Disclosedexample apparatus include the processor programmed to, when thesimilarity value satisfies a first threshold, determine that the mediapresentation device is in a first state. Disclosed example apparatusinclude the processor programmed to, when the similarity value does notsatisfy the first threshold, determine that the media presentationdevice is in a second state. Disclosed example apparatus include theprocessor programmed to control a metering device based on the state ofthe media presentation device.

Examples disclosed herein provide methods of determining a state of amedia presentation device. Disclosed example methods include generatinga first set of weighted coefficients based on first audio received byfirst and second microphones at a first time. Disclosed example methodsinclude generating a second set of weighted coefficients based on secondaudio received by the first and second microphones at a second timeafter the first time. Disclosed example methods include calculating adot product between the first set of coefficients and the second set ofcoefficients. Disclosed example methods include, when the calculated dotproduct satisfies a threshold, determining that the media presentationdevice is in a first state (e.g., turned on or activated, etc.).Disclosed example methods include, when the calculated dot product doesnot satisfy the threshold, determining that the media presentationdevice is in a second state (e.g., turned off or powered down, etc.).

Examples disclosed herein provide tangible computer readable storagemedia having instruction that, when executed, cause a machine togenerate a first set of weighted coefficients based on first audioreceived by first and second microphones at a first time. Disclosedexample computer readable storage media have instructions that, whenexecuted, further cause the machine to generate a second set of weightedcoefficients based on second audio received by the first and secondmicrophones at a second time after the first time. Disclosed examplecomputer readable storage media have instructions that, when executed,further cause the machine to calculate a dot product between the firstset of coefficients and the second set of coefficients. Disclosedexample computer readable storage media have instructions that, whenexecuted, further cause the machine to, when the calculated dot productsatisfies a threshold, determine that the media presentation device isin a first state. Disclosed example computer readable storage media haveinstructions that, when executed, further cause the machine to, when thecalculated dot product does not satisfy the threshold, determine thatthe media presentation device is in a second state.

Examples disclosed herein provide apparatus including a metering device.Disclosed example metering devices are to generate a first set ofweighted coefficients based on first audio received by first and secondmicrophones at a first time. Disclosed example metering devices are togenerate a second set of weighted coefficients based on second audioreceived by the first and second microphones at a second time after thefirst time. Disclosed example metering devices are to calculate a dotproduct between the first set of coefficients and the second set ofcoefficients. Disclosed example metering devices are to, when thecalculated dot product satisfies a threshold, determine that the mediapresentation device is in a first state. Disclosed example meteringdevices are to, when the calculated dot product does not satisfy thethreshold, determine that the media presentation device is in a secondstate.

Examples disclosed herein provide methods of determining a sourcelocation for an audio signal. Disclosed example methods includeprocessing a captured audio signal to generate a first set of weightedcoefficients characterizing the captured audio signal. Disclosed examplemethods include comparing the first set of weighted coefficients to asecond set of weighted coefficients representing a reference audiosignal originating from a first location to generate a similarity value.Disclosed example methods include, when the similarity value satisfies acomparison threshold, identifying a source location of the capturedaudio signal as the first location. Disclosed example methods include,when the similarity value does not satisfy the comparison threshold,identifying the source location of the captured audio signal as a secondlocation.

Examples disclosed herein provide a metering device including aprocessor particularly programmed to at least determine a sourcelocation for an audio signal by processing a captured audio signal togenerate a first set of weighted coefficients characterizing thecaptured audio signal. The processor of the disclosed example meteringdevice is programmed to at least determine a source location for anaudio signal by comparing the first set of weighted coefficients to asecond set of weighted coefficients representing a reference audiosignal originating from a first location to generate a similarity value.The processor of the disclosed example metering device is programmed toat least determine a source location for an audio signal by, when thesimilarity value satisfies a comparison threshold, identifying a sourcelocation of the captured audio signal as the first location. Theprocessor of the disclosed example metering device is programmed to atleast determine a source location for an audio signal by, when thesimilarity value does not satisfy the comparison threshold, identifyingthe source location of the captured audio signal as a second location.

Examples disclosed herein include example systems and methods fordetermining whether or not the audio captured by a monitoring device isfrom a media presentation device of interest when measuring audiencemember exposure to media. In the examples disclosed herein, twomicrophones are separated by a fixed distance to monitor the audiopresent in a room. Audio emanating from different locations within theroom create audio signals detected by the microphones that are timedelayed with respect to each other and have different multi-pathinterference effects that are controlled by room acoustics as well asaudio source location. In the examples disclosed herein, the differencesbetween the audio signals are analyzed by an adaptive equalizationalgorithm using a finite impulse response (FIR) filter. The examplefilter taps or coefficients generated by applying the FIR filterconstitute a feature vector (e.g., a set of coefficients) thatcharacterizes the audio source (e.g., an identity and/or location of theaudio source, etc.). In the examples disclosed herein, the set ofcoefficients obtained while the media presentation device is ON is usedas a vector to uniquely identify future audio signals from the mediapresentation device as well as to reject audio signals from othersources. An example baseline (e.g., reference) vector is generatedduring an interval in which the audio from the media presentation deviceis confirmed by the successful extraction of watermarks embedded in themedia presented by the media presentation device, for example.

Example Monitoring and Determination Systems

FIG. 1 depicts an example system 100, depicted in an example room 110having a media presentation device 120 (e.g., a television, otherdisplay or monitor, etc.) including speakers 122, 124 providing audio126 in the room 110. While the example of FIG. 1 depicts a typical room110 in a household, the room 110 can be any space, public or private,including a home, restaurant, bar, vehicle, bus, boat, etc. The examplesystem 100 includes an example metering device 130 including a pair ofmicrophones 132 and 134, a source detector 136, a decoder 138, and acreditor 139.

The example source detector 136 of the metering device 130 determines astate of the example media presentation device 120. The metering device130 of the illustrated example is disposed on or near the mediapresentation device 120 and may be adapted to perform one or more of aplurality of metering methods (e.g., channel detection, watermarkdetection, collecting signatures and/or codes, etc.) to collect dataconcerning the media exposure of the metering device 130, and thus, themedia exposure of one or more audience member(s) 140, 142 with respectto the media presentation device 120.

Depending on the type(s) of metering that the metering device 130 isadapted to perform, the metering device 130 may be physically coupled tothe media presentation device 120 or may instead be configured tocapture signals emitted externally by the media presentation device 120such that direct physical coupling to the media presentation device 120is not required. For instance, in this example, the metering device 130is not physically or electronically coupled to the monitored mediapresentation device 120. Instead, the metering device 130 is providedwith at least one audio sensor, such as, for example, a microphone, tocapture audio data regarding in-home media exposure for the audiencemember(s) 140, 142. Similarly, the example metering device 130 isconfigured to perform one or more of a plurality of metering methods(e.g., collecting watermarks, signatures and/or codes, etc.) on thecollected audio to enable identification of the media to which theaudience members 140, 142.

As shown in more detail in the example of FIG. 2, the metering device130 is provided with two example microphones 132 and 134, separated by adistance D_(m), to record an audio signal 126 from the mediapresentation device 120. The example media presentation device 120(e.g., a television set) has two speakers 122 and 124. Both speakers 122and 124 reproduce or emit the audio signal 126 that is sensed by themicrophones 132 and 134. In a typical room 110, multiple reflections mayoccur and the resulting audio signal 126 sensed by microphones 132 and134 represents the combined effect of a direct wave and thesereflections. Similarly, audience members 140, 142 talking to each in theroom 110 other can create audio signals 144, 146 that are sensed by themicrophones 132 and 134, as direct waves and/or as reflections.

In order to identify a single audio signal for metering, the examplesource detector 136 uses an algorithm, such as an adaptive least meansquare algorithm, to construct a finite impulse response (FIR) filterthat attempts to convert the audio (e.g., the audio signal 126, 144,and/or 146) captured by the first microphone 132 to a new syntheticsignal (also referred to herein as a filtered audio signal) that is asclose as possible, in a least mean squared sense, to the audio capturedby the second microphone 134. Coefficients or “filter taps” of thecorresponding adaptive filter depend on, for example, a location of theaudio/media source (e.g., the media presentation device 120, theaudience members 140, 142, etc.) of audio (e.g., the audio signal 126,144, and/or 146, etc.) within the room 110 (e.g., assuming there is asingle source). For example, a media presentation device 120 (e.g., atelevision set, etc.) equipped with two or more speakers (e.g., thespeakers 122, 124, etc.) is considered to be a single source such thatsubstantially the same audio waveform emanates from the speakers 122,124 connected to the media presentation device 120). Coefficients or“filter taps” of the corresponding adaptive filter also depend on, forexample, a separation between the microphones (D_(m)). Coefficients or“filter taps” of the corresponding adaptive filter also depend on, forexample, acoustic characteristics of the room 110 determined by wallsand/or other objects in the room 110 (e.g., devices, furniture, etc.).The source detector 136 assumes microphone separation and acousticcharacteristics are constant for the coefficients that characterize theaudio source.

As described further below, an adaptive algorithm subtracts the filteredversion of the audio signal 126 received by the microphone 132 from theaudio signal 126 received by the microphone 134. An adaptive filtergenerates the filter taps or coefficients used in the adaptive algorithmto yield information about similarity (or lack thereof) between theaudio signals received by the microphones 132, 134, wherein the signalfrom the microphone 132 is delayed and filtered and the signal from themicrophone 134 is not. Once the audio has been processed, the audio(e.g., audio signal 126, 144, 146, etc.) can be analyzed to determine alikely source of that audio and its associated status.

The example metering device 130 includes a decoder 138 and a creditor139 in addition to the source detector 136 and microphones 132, 134. Thedecoder 138 receives and decodes audio received by the microphones 132and 134 to extract a watermark and/or other code from the receivedaudio. The decoder 138 works with the source detector 136 to determinewhether or not the received audio is associated with the mediapresentation device 120 or from other ambient sound sources, such asaudience members 140, 142, audio from another device, etc. If thedecoder 138 identifies a valid watermark, for example, then the creditor139 captures an identification of the media exposure and/or otheraudience measurement data based on the decoded watermark, code, etc. Insome examples, the watermark is associated with a score (e.g., areliability score) indicating a strength of or confidence in the decodedwatermark. In some examples, a volume analysis is done in conjunctionwith the watermark identification to confirm that the watermark has beengenerated by a nearby monitored source instead of from a source that isfarther away (e.g., spillover). In some examples, a signature and/orother code can be computed from the audio instead of or in addition tothe watermark (e.g., when a watermark is not identified in the audio,etc.). The creditor 139 may also assign a location identifier,timestamp, etc., to the decoded data.

In certain examples, a home processing system (not shown) may be incommunication with the meter 130 to collect media/audience exposure data(e.g., watermark, signature, location, timestamp, etc.) from themetering device 130 for further analysis, relay, storage, etc. As shownin the example of FIG. 1, data gathered by the meter 130 (e.g.,watermark, signature, location, timestamp, etc.) can be relayed (e.g.,directly or via the home processing system, etc.) via a network 150 toan audience measurement entity (AME) 160, such as a data collectionfacility of a metering entity (e.g., The Nielsen Company (US), LLC) forfurther tracking, analysis, storage, reporting, etc. Data can be relayedto the AME 160 via the network 150 (e.g., the Internet, a local areanetwork, a wide area network, a cellular network, etc.) via wired and/orwireless connections (e.g., a cable/DSL/satellite modem, a cell tower,etc.).

In the illustrated example of FIG. 1, the AME 160 includes a monitoringdatabase 162 in which to store the information from the monitored room110 and a server 164 to analyze data aggregated in the monitoringdatabase 162. The example AME 160 may process and/or store data receivedfrom other metering device(s) (not shown) in addition to the meteringdevice 130 such that a plurality of audience members can be monitoredand evaluated. In another example, multiple servers and/or databases maybe employed as desired.

For example, the example server 164 collects the media exposure datafrom the meter 130 and stores the collected media exposure data in theexample monitoring database 162. The example server 164 processes thecollected media exposure data by comparing the codes, metadata, and/orsignatures in the collected media exposure data to reference codesand/or signatures in a reference database to identify the media and/orstation(s) that transmit the media. Examples to process the codes and/orsignatures in the collected media exposure data are described in U.S.patent application Ser. No. 14/473,670, filed on Aug. 29, 2014, which ishereby incorporated herein by reference in its entirety. The exampleserver 164 awards media exposure credits to media identified in thecollected media exposure data, for example. In some examples, the mediaexposure credits are associated with demographic informationcorresponding to the audience member 140, 142 and/or type of audiencemember 140, 142 associated with the meter 130 that collected the mediaexposure data.

FIG. 3 illustrates an example implementation of the example sourcedetector 136 of FIG. 2. In the illustrated example of FIG. 3, theexample source detector 136 includes an adaptive audio filter 302, aweight adjuster 304, an audio comparator 306, and a state determiner308. The example adaptive audio filter 302 samples audio received by thefirst microphone 132 at a specified sampling rate. In some examples, theadaptive audio filter 302 uses a 48 kHz sampling rate and uses 512filter taps or weights (e.g., with values starting at zero andincreasing based on signal delay). In such examples, the 512 filter tapsor weights correspond to a maximum time delay of 10.66 milliseconds (1/48000 second per sample×512 samples). Because the microphones 132, 134are close to one another (e.g., distance D_(m)) relative to a distancebetween the meter 130 and the potential audio sources 120, 140, 142, adelay of the audio signal 126 is below a maximum time delay value (e.g.,10.66 milliseconds). In some examples, other sampling rates, such as 24kHz, may be used. In such examples, the filter weights used by theadaptive audio filter 302 become relatively constant values in, forexample, less than a second from the start of the detected audioprocessing.

The set of weights (e.g., {W_(m), m=0, 1, . . . M−1}, also referred toas a feature vector) generated by the weight adjuster 304 characterizesa particular source of audio. In some examples, filter weights aremodified and/or generated by the weight adjuster 304 based on feedbackfrom the audio comparator 306 and/or other external input regarding theaudio signal 126.

The audio comparator 306 analyzes the filtered audio signal provided bythe adaptive audio filter 302 from the microphone 132 and compares it tothe unfiltered audio signal input from the microphone 134. An errorsignal, used to impact the set of filter coefficients, can be alsogenerated by the audio comparator 306 based on the comparison. If theaudio comparator 306 matches the filtered and unfiltered signals anddetermines that the filtered audio detected by the first microphone 132is substantially the same as the unfiltered audio detected by the secondmicrophone 134, the error signal decreases to a low value. If, however,the filtered audio from the first microphone 132 is distinct from theunfiltered audio from the second microphone 134, the error signalincreases in value. An increase in the error signal can trigger are-calculation of the filter weight coefficients by the adaptive audiofilter 302 to help ensure that the filtered audio from the microphone132 matches the unfiltered audio from the microphone 134 and properlycharacterizes the audio source. The coefficients characterizing thataudio source can then be compared to a reference to determine whetherthe audio source is the media presentation device 120 or is some othersource of audio (e.g., people 140, 142 talking, ambient noise, anotherdevice emitting sound in the same room and/or another room, etc.).

In the illustrated example of FIG. 3, the adaptive audio filter 302and/or the audio comparator 306 examines the filter coefficientdistribution to help ensure that the filter taps or weights generated bythe weight adjuster 304 have a decay characteristic (e.g., in whichcoefficient weights “decay” or reduce to zero over time). In theillustrated example, audio received by the second microphone 134 isdelayed relative to the audio received by the first microphone 132 whenthe media presentation device 120 is the source of audio (e.g., theaudio 126 of FIG. 1) because the first microphone 132 is relativelycloser (in the example of FIG. 1) to the media presentation device 120than the second microphone 134. Phrased differently, a relative delaybetween audio detected by the first and second microphones 132, 134 isdependent on the location of the meter 130 relative to the mediapresentation device 120. In some examples, the relative delay is basedis also based on multiple sound wave paths in a room (e.g., the room 110of FIG. 1) due to walls and other objects that contribute to acousticreflections.

The example weight adjuster 304 generates and/or updates a set of filtercoefficients having a magnitude that exponentially decreases as theindex increases (e.g., a decay characteristic). An example filter weightdistribution corresponding to filter weights that indicate that measuredaudio matches characteristics of audio emitted by the monitoredpresentation device 120, and, therefore, indicate that the monitoredmedia presentation device 120 is turned on is shown in FIG. 4.

As shown in the example of FIG. 4, an example filter weight distribution402 includes a plot of a plurality of filter weight coefficients 404along an index 406, such as time (e.g., milliseconds), sample, etc.Individual weights 408-413 along the distribution form the set of filtercoefficients used as a feature vector to represent the audio andcalculate similarity between the audio signal and a reference vector.

The example audio comparator 306 of FIG. 3 calculates a signal errorbetween the audio signal received by the first microphone 132 and thenfiltered and the audio signal received by the second microphone 134. Asdescribed above, if the audio comparator 306 matches the filtered andunfiltered signals and determines that the filtered audio detected bythe first microphone 132 is substantially the same as to the unfilteredaudio detected by the second microphone 134, the error signal has a lowvalue (e.g., close to zero), and the associated weight coefficients canbe used for analysis. If, however, the filtered audio from the firstmicrophone 132 is distinct from the unfiltered audio from the secondmicrophone 134, the error signal has a high value (e.g., one), andassociated weight coefficients may be unsuitable for analysis. Theexample weight adjuster 304 adjusts and/or recalculates the weightedcoefficients generated by the adaptive audio filter 302 based on theerror signal till the filtered and unfiltered signals match and can thenbe compared to a reference to determine whether the monitoredpresentation device 120 is the source of the detected audio (and istherefore inferred to be “on” or “off”, for example).

For example, filter weight coefficients are determined by an echocancellation algorithm, described further below with respect to FIG. 6,so that, if the microphone 132 output is filtered using thesecoefficients and added to the microphone 134 signal, the net result is avery low energy audio output. Under ideal conditions, signals from 132(filtered) and 134 (unfiltered) should perfectly cancel one another. Inreality, however, the difference between filtered and unfilteredmicrophone inputs should be close to zero if the signals from 132 and134 are both indicative of audio from the media presentation device 120.

In an example, suppose a difference in signals between microphone 132and microphone 134 is a constant delay equivalent to ten samples. Inthis example, an FIR filter includes a single high value at a tenthcoefficient and remaining filter coefficients are zero. However, roomacoustics may complicate the filter coefficient analysis (e.g.,remaining filter coefficients may be low but not exactly zero). Thus, insuch an example, results include a primary audio “beam” with a delay often samples when measured at microphone 132 relative to microphone 134.The primary beam provides a high value coefficient at index 10 (e.g.,corresponding to the delay of ten samples) followed by several smallercoefficients at indexes 11, 12, etc.

After the inputs from microphones 132 and 134 have been compared toreconcile the audio and generate the set of filter coefficients, theexample audio comparator 306 provides the set of filter coefficients tothe example state determiner 308. The state determiner 308 compares theset of filtered coefficients associated with the received audio signal126 to a stored representation (e.g., a reference set of filtercoefficients) of a reference signal indicating that the mediapresentation device 120 is turned on. If the received audio signal 126matches or closely approximates characteristics of the reference signal(e.g., based on a comparison of the coefficient sets resulting ingeneration of a similarity value), then the state determiner 308 infersthat the media presentation device 120 is turned on and outputting theaudio 126. That is, characteristics of the audio signal 126, asidentified by its set of filter coefficients, indicate a location ofsource for the audio signal 126 that matches a predetermined or “known”location of the media presentation device 120 as indicated by thereference set of filter coefficients.

Otherwise, the state determiner 308 infers that the media presentationdevice 120 is turned off or is otherwise not outputting detectable audio(e.g., is muted, the volume is turned down past a detectable threshold,etc.). Thus, a source of the audio detected by the microphones 132, 134is other than the media presentation device 120. In some examples, asdescribed further below, a mathematical operation such as a dot productdetermines a similarity of characteristics between the weightcoefficients of the detected audio and reference coefficients. In someexamples, reference coefficients can be recalculated by the weightadjuster 304 to accommodate a re-positioning of the media presentationdevice 120, an introduction of a new audio source in the room 110, etc.Recalculation or recalibration of reference coefficients can be based onone or more factors such as a time period without detecting audio havingsimilar coefficients to the reference, identification of a validwatermark having a high reliability score, passage of a defined periodof time (e.g., based on statistical analysis of prior media exposuredata, household characteristic, etc.), etc.

When the meter 130 is extracting audio watermarks embedded in thedetected audio, the state determiner 308 infers that the mediapresentation device 120 is likely the source (or a significantcontributor) of the detected audio. As mentioned above, periodicallyand/or at certain defined times (e.g., as preset in the source detector136, based on accuracy of watermark detection, based on quality offeedback, etc.), the example state determiner 308 stores filtercoefficients generated by the example adaptive audio filter 302 andweight adjuster 304 during respective intervals as baseline (e.g.,reference) filter coefficients {W_(m1), m=0, 1, . . . M−1}.

In some examples, if the state determiner 308 determines that thebaseline filter coefficients generated by the weight adjuster 304 do notexhibit a decay characteristic (e.g., decreasing as the indexincreases), the example adapter audio filter 302 and the example audiocomparator 306 interchange the audio signals received by the first andsecond microphones 132 and 134 (e.g., so that now the signal received atthe second microphone 134 is filtered and the signal received at thefirst microphone 132 remains unfiltered) and rerun the adaptationalgorithm to obtain a more suitable set of baseline coefficients thatexhibit a decay characteristic. The signals are interchanged to correctan assumption in relative position and delay between the mediapresentation device 120 and the microphones 132 and 134. For example,the filter coefficients generated by the weight adjuster 304 may notexhibit a decay characteristic when the signal from microphone 132 isprocessed relative to the signal from microphone 134 because therelative positions of the microphones 132, 134 with respect to the mediapresentation device 120 are such that audio received by the firstmicrophone 132 is delayed relative to audio received by the secondmicrophone 134 (rather than the initial assumption that audio receivedfrom microphone 134 is delayed with respect to audio received bymicrophone 132).

In some examples, when the media presentation device 120 is turned off,there may be other sources of audio in the room 110, such as audiencemembers 140, 142 talking to each other. In such examples, the weightadjuster 304 generates a new or updated set of coefficients {W_(m2),m=0, 1, . . . M−1} for the audio 144, 146. FIG. 5 shows filtercoefficients generated by the weight adjuster 304 corresponding tofilter weights that indicate that measured audio does not matchcharacteristics of audio emitted by the monitored presentation device,and, therefore, the monitored media presentation device is turned downor off in such an example.

As shown in the example of FIG. 5, an example filter weight distribution502 includes a plot of a plurality of filter weight coefficients 504along an index 506, such as time (e.g., milliseconds), sample, etc.Individual weights 508-513 along the distribution form the set of filtercoefficients used as a feature vector to represent the audio andcalculate similarity between the audio signal and a reference vector.

Based on the filter weight coefficients from the weight adjuster 304 andsignal comparison by the audio comparator 306, the state determiner 308determines whether or not the monitored media presentation device 120is 1) turned on or 2) turned off, down, or muted such that no audiosignal 126 can be detected by the microphones 132, 134.

FIG. 6 shows an example filter apparatus 600 which can be included, forexample, in the source detector 136 of the metering device 130 (e.g.,illustrated in FIGS. 1-3) to help identify a source of audio detected bythe microphones 132, 134 of the metering device 130. As shown in theexample of FIG. 6, the example filter apparatus 600 can be used toimplement all or part of the adaptive audio filter 302, weight adjuster304, and audio comparator 306. The example filter apparatus 600 shown inFIG. 6 implements an adaptive echo cancellation algorithm and isconfigured to subtract a filtered version of detected audio recorded bythe first microphone 132 from the detected audio recorded by the secondmicrophone 134. In some examples, the microphones 132, 134 may also havepicked up other ambient audio, including the human speech 144, 146.

As disclosed in more detail below, the adaptive filter 600 (e.g., anadaptive Finite Impulse Response (FIR) filter, etc.) generates filtercoefficients or taps which, upon analysis, yield information about asimilarity between the audio signals detected by the microphones 132,134. Depending on a location of the source, the audio received by themicrophone 134 may be delayed relative to the audio received by themicrophone 132, or vice versa. In some examples, multiple sound wavepaths exist due to reflections from walls and other objects in the room.Therefore, in order to subtract the effect of the audio detected by thefirst microphone 132 from the audio detected by second microphone 134, aFIR filter is used to delay and/or attenuate the audio detected by thefirst microphone 132, for example.

In the example illustrated in FIG. 6, audio samples 602 received by themicrophone 132 are passed through a delay line 604, which includes a setof M shift registers D. In the illustrated example, X_(M−1), is a mostrecent sample, and X₀ is an oldest sample. An output Y₀ of the filter isshown in Equation 1 below:

$\begin{matrix}{Y_{o} = {\sum\limits_{m = 0}^{m = {M - 1}}{W_{m}{X_{m}.}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$In Equation 1 above, {W_(m), m=0, 1, . . . M−1} are weights whoseinitial values are set to 0. The set of weights may define a feature orreference vector. A current unfiltered input sample of audio 606 fromthe second microphone 134 is X_(d). In the illustrated example of FIG.6, by summing 608 the weighted audio samples X_(M−1) to X₀ as shiftedusing shift registers D, the filter apparatus 600 operates to make theoutput Y₀ of the filter from the audio sample 602 of microphone 132approximate the current input sample X_(d) from microphone 134 (e.g.,Y₀≈X_(d)).

To verify the approximation, a difference 610 is obtained in comparingthe filter output Y₀ to X_(d) to generate a difference signal X_(e)indicative of an error in the approximation. For example, if Y₀≈X_(d),then X_(e) should be at or near 0. However, if Y₀≈X_(d), then X_(e) willbe greater than 0. To help ensure that the approximation of Y₀ to X_(d)holds true, the weights W_(M−1), W_(M−2), W_(M), . . . , W₀ are adjusted612 to new values based on an error signal X_(e) generated, for example,as shown in Equations 2 and 3 below:X _(e)(n)=X _(d)(n)−Y ₀(n)  Equation 2;W _(m)(n+1)=W _(m)(n)+μX _(e) X _(m)(n)  Equation 3.

In Equations 2 and 3 above, an index n is an iteration index denoting atime, indicated in sample counts, at which the modification in weightsis made, and μ is a learning factor that is usually set to a low value(e.g., 0.05, etc.) in the illustrated example. This learning factorgradually minimizes a least mean squared (LMS) error in the outputcomparison as the filter output converges over the n iterations.

In certain examples, to determine a state (e.g., turned on or turnedoff) of the media presentation device 120, the example state determiner308 calculates a dot product between a reference vector of filtercoefficients and a comparison vector of filter coefficients {W_(m1),W_(m2)} and compares the result (e.g., referred to as a similarityvalue, comparison value, etc.) to a threshold (e.g., 0.5, etc.):

$\begin{matrix}{{A \cdot B} = {{\sum\limits_{m = 0}^{m = {M - 1}}{A_{m}B_{m}}} = {{A_{0}B_{0}} + {A_{1}B_{1}} + {\ldots\mspace{14mu} A_{M - 1}{B_{M - 1}.}}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$The dot product (e.g., shown in Equation 4 between corresponding Mcoefficients of vectors A and B) or other similar mathematicalcomparison between a) a known reference set of coefficients indicativeof audio characteristics from an audio source (e.g., the mediapresentation device 120) at a particular location in the room 110 and b)a second set of coefficients indicative of audio characteristics from acaptured audio signal determines whether or not the captured audiooriginated from the same source as the reference audio (e.g., whetherthe audio signal 126 came from the media presentation device 120 or wasinstead generated by another source such as people 140, 142 in the room110, etc.). If the analysis determines that the audio signal 126originated from the media presentation device 120, then it can beinferred that the media presentation device 120 is turned on. Otherwise,it can be inferred that the media presentation device 120 is turned offor muted or turned down such that audio is not detectable from the mediapresentation device 120 by the microphones 132, 134. Audio that does notemanate from the media presentation device 120 is not metered, forexample.

In the illustrated example, the threshold for dot product comparison maybe specified (e.g., by a user, by a programmer, based on feedback fromanother application, etc.) to achieve a desired level of accuracy inidentifying whether the media presentation device 120 is “ON” or “OFF”.If the result of the dot product satisfies (e.g., is greater than) thethreshold, the example state determiner 308 determines that the mediapresentation device 120 is ON. For example, if the media presentationdevice 120 is ON, the result of the dot product may be close to 1.0(e.g., assuming that the meter 130 has not been moved since the baselinecoefficients were last calculated). If the result of the dot productdoes not satisfy (e.g., is less than) the threshold, the example statedeterminer 308 determines that the media presentation device 120 is OFF.

In certain examples, the dot product is calculated by converting eachset of filter coefficients to a unit vector. Thus, the set of filtercoefficients can be normalized for comparison between a comparisonvector of measured filter coefficients and a reference vector of knownfilter coefficients. The unit vector or “normalized vector” represents aspatial direction in n-dimensional space. Then, the dot product iscalculated using the unit vectors to determine an output in a rangebetween negative one and positive one (−1.0 to +1.0). This output can beused to identify or otherwise characterize a source of the audio signal126 and, extension, determine whether the monitored media presentationdevice 120 is outputting detectable audio (e.g., is turned on) or is notoutputting detectable audio (e.g., is turned off or has its volumeturned down or muted such that the media presentation device 120 is notoutputting audio detectable by the meter 130).

The dot product or “scalar product” of two unit vectors in n-dimensionalspace is a scalar value calculated from a sum of the products of thecorresponding n elements in each of the two unit vectors. If the twounit vectors are distinct unit vectors (e.g., orthogonal inn-dimensional space), then their dot product is zero. However, if thetwo unit vectors are identical, then their dot product is one.Therefore, two unit vectors that are different will have almost no dotproduct (e.g., close to 0), while two unit vectors that are the same orsimilar will have a dot product close to +1.0.

For example, suppose a reference vector obtained when the mediapresentation device 120 is confirmed “on”, such as the example set offilter coefficients from the example of FIG. 4, is represented asreference vector R={900, 100, −300, 0, 0, 0}. A first comparison filtercoefficient vector C1={910, 120, −310, 0, 0, 0}. A second comparisonfilter coefficient vector, modeled after the example filter coefficientsin the example of FIG. 5, C2={100, 0, 0, −200, 100, 200}.

In certain examples, coefficient vectors are normalized or converted tounit vectors as follows: u=v/|v|, where u represents the unit vector, vrepresents the original vector, and |v| represents a magnitude of thevector v. For the first comparison vector C1, its unit vector can bedetermined as follows (values truncated for purposes of the illustrativeexample):|C1|=√{square root over ((910)²+(120)²+(−310)²+0+0+0)}=968.81, and

-   -   Unit vector CU1={910/968.81, 120/968.81, −310/968.81, 0, 0, 0}.        For the second comparison vector C2, its unit vector can be        determined as follows:        |C2|=√{square root over        ((100)²+0+0+(−200)²+(100)²+(200)²)}=316.23,        and        Unit vector        CU2={100/316.23,0,0,−200/316.23,100/316.23,200/316.23}.        For the reference vector R, its unit vector can be determined as        follows:        |R|=√{square root over ((900)²+(100)²+(−300)²+0+0+0)}=953.94,        and        Unit vector RU={900/953.94,100/953.94,−300/953.94,0,0,0}.

Using the above example values and Equation 4, a dot product of RU andCU1 can be computed as follows:RU·CU1=(910/968.81*900/953.94)+(120/968.81*100/953.94)+(−310/968.81*−300/953.94)+0+0+0=0.999798.Comparing the dot product result to the example threshold of 0.5 showsthat the dot product of RU and CU1 is greater than the threshold of 0.5and close to 1.0. As described above, such a result indicates that theaudio signal 126 is emanating from the media presentation device 120,and, therefore, the media presentation device 120 is inferred to beturned on.

Similarly, a dot product of coefficient unit vector CU2 and referenceunit vector RU can be determined as follows:

RU ⋅ CU 2 = (100/316.23 * 900/953.94) + (0 * 100/953.94) + (0 * −300/953.94) + (−200/316.23 * 0) + (100/316.23 * 0) + (200/316.23 * 0) = 0.30.Comparing the dot product result to the example threshold of 0.5 showsthat the dot product of RU and CU2 is less than the threshold of 0.5 andclose to 0. As described above, such a result indicates that the audiosignal 126 is not emanating from the media presentation device 120, and,therefore, the media presentation device 120 is inferred to be turnedoff, turned down, or muted so as to not be detectable by the microphones132, 134.

In the illustrated example, the weight adjuster 304 generates a new setof baseline filter coefficients W_(m1) periodically and/or occasionally(e.g., every thirty seconds, every thirty minutes, upon receivingfeedback from the decoder 138, creditor 139, and/or AME 160 for bad orotherwise inaccurate result, etc.). For example, the weight adjuster 304can generate a new reference vector of filter coefficients periodically,in response to a certain number or time period of “off” determinations,in response to a confirmed watermark extraction, etc. For example, theweight adjuster 304 can periodically recalibrate by calculating thereference vector of filter coefficients when a watermark analysisconfirms that the media presentation device 120 is turned “on” andemitting valid audio data. The baseline or reference coefficients W_(m1)may be stored for use by the adaptive audio filter 302, the audiocomparator 306, and the state determiner 308 in subsequent audio signalanalysis and dot product computation, for example.

FIG. 7 shows an example implementation of the state determiner 308 shownand described above with respect to the example of FIG. 3. As shown inthe example of FIG. 7, the example state determiner 308 includes acomparison vector 702, a reference vector 704, a coefficient comparator706, and a state machine 708 to receive filter weight coefficients fromthe audio comparator 306, determine a status or operating state of themonitored media presentation device 120, and output the determineddevice state to the decoder 138 and/or creditor 139, as described abovewith respect to FIGS. 1-3.

As described above, after the inputs from microphones 132 and 134 havebeen compared to reconcile the audio and generate the set of filtercoefficients, the example audio comparator 306 provides a comparison setof filter coefficients to the example state determiner 308. The set ofcoefficients forms the comparison vector 702 (e.g., converted to a unitvector as described above). The state determiner 308 receives a baselineor reference set of vector coefficients (e.g., from a known or confirmed“on” state of the media presentation device) from the weight adjuster304 and/or otherwise stores reference coefficients as the referencevector 704 (e.g., converted to a unit vector as described above). Thecoefficient comparator 706 compares the comparison vector 702 to thereference vector 704 to determine if the filter weight characteristicsof the currently captured audio are similar to the referencecharacteristics of the “known” media presentation device 120 audio. Forexample, a dot product of the comparison vector 702 and the referencevector 704 yields a value indicative of the similarity or dissimilaritybetween the comparison vector 702 and the reference vector 704 (and,thereby, the current audio signal 126 and the previously evaluatedreference audio signal).

Based on the similarity value determined by the coefficient comparator706, the state machine 708 infers and/or otherwise determines a state orstatus of the monitored media presentation device 120. For example, ifthe similarity value indicates that the comparison vector 702 and thereference vector 704 are identical or similar (e.g., a dot product ofthe vectors 702, 704 is greater than a threshold (0.5, for example),such as near 1.0), then the state machine 708 indicates or infers thatthe monitored media presentation device 120 is turned on and is thesource of the detected audio signal 126. However, if the similarityvalue indicates that the comparison vector 702 and the reference vector704 are not similar (e.g., a dot product of the vectors 702, 704 is lessthan the threshold, such as near 0), then the state machine 708indicates or infers that the monitored media presentation device 120 isturned off or is otherwise not outputting detectable audio (e.g., ismuted, the volume is turned down past a detectable threshold, etc.).Thus, a source of the audio detected by the microphones 132, 134 isother than the media presentation device 120. The state of the mediapresentation device 120 provided by the state machine 708 (e.g., on,off, etc.) is sent to the decoder 138 and/or creditor 139 to furtherprocess or not process the detected audio (e.g., to process the audiosignal 126 from the media presentation device 120 to extract awatermark, calculate a signature, and/or otherwise determine mediaexposure data, for example).

While an example manner of implementing the source detector 136 andfilter apparatus 600 are illustrated in FIGS. 1-3 and 6-7, one or moreof the elements, processes and/or devices illustrated in FIGS. 1-3 and6-7 may be combined, divided, re-arranged, omitted, eliminated and/orimplemented in any other way. Further, the example adaptive audio filter302, the example weight adjuster 304, the example audio comparator 306,the example state determiner 308, and/or, more generally, the examplesource detector 136 and/or example filter apparatus 600 of FIGS. 1, 2,3, and 6-7 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example adaptive audio filter 302, the example weightadjuster 304, the example audio comparator 306, the example statedeterminer 308, and/or, more generally, the example source detector 136and/or the filter apparatus 600 can be implemented by one or more analogor digital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example adaptive audio filter 302, the example weight adjuster 304,the example audio comparator 306, the example state determiner 308, andthe example filer apparatus 600 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example source detector 136 and filter apparatus 600 of FIGS. 1, 2,3, 6, and 7 may include one or more elements, processes and/or devicesin addition to, or instead of, those illustrated in FIGS. 1-3 and 6-7,and/or may include more than one of any or all of the illustratedelements, processes and devices.

Example Monitoring and Determination Methods

Flowcharts representative of example machine readable instructions forimplementing the example source detector 136 and filter apparatus 600 ofFIGS. 1-3 and 6-7 are shown in FIGS. 8-10. In this example, the machinereadable instructions comprise a program for execution by a processorsuch as the processor 1112 shown in the example processor platform 1100discussed below in connection with FIG. 11. The program may be embodiedin software stored on a tangible computer readable storage medium suchas a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), a Blu-ray disk, or a memory associated with the processor 1112,but the entire program and/or parts thereof could alternatively beexecuted by a device other than the processor 1112 and/or embodied infirmware or dedicated hardware. Further, although the example program isdescribed with reference to the flowcharts illustrated in FIGS. 8-10,many other methods of implementing the example source detector 136 andfilter apparatus 600 of FIGS. 1-3 and 6-7 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 8-10 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 8-10 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 8 is a flow diagram representative of example machine readableinstructions 800 that may be executed to implement a monitoring andaudience measurement process including the example metering device 130of FIG. 1 and its components (see, e.g., FIGS. 1, 2, 3, and 6-7). Atblock 802, a reference vector of filter coefficients is determined aspart of a calibration of the source detector 136 for audio monitoring inthe room 110. For example, the echo cancellation algorithm of Equations1-3 and the adaptive filter apparatus 600 are applied by the exampleadaptive audio filter 302 (FIG. 3) to known audio captured from each ofthe plurality of microphones 132, 134. As disclosed above with respectto FIGS. 1-7, a baseline or reference vector (e.g., such as thereference vector 704 of the example of FIG. 7) of filter coefficients isgenerated by the source detector 136 (e.g., the example weight adjuster304 of the example of FIG. 3) for use in determining an identity and/orlocation of an audio source (e.g., the media presentation device 120,audience member 140, and/or audience member 142).

At block 804, the example metering device 130 (FIG. 1) monitors the room110 to detect and capture audio via multiple microphones 132 and 134.For example, microphones 132, 134 in the example meter 130 operate tocapture audio 126, 144, and/or 146 audible within range of themicrophones 132, 134 in the room 110.

In some examples, before advancing to block 806, captured audio isanalyzed to identify a watermark in the captured audio signal. If awatermark is found, then, the watermark information can be used toidentify media content associated with the audio (e.g., at block 814below). In some examples, a reliability score associated with thewatermark can also be evaluated to determine whether the watermark isreliable enough for use in media identification. For example, if awatermark is detected from audio emanating from another device in anadjacent room (e.g., a device other than the monitored mediapresentation device 120), then the distance (and its associated noiseaffect) from the metering device 130 and its microphones 132, 134 canresult in an inadvertently detected watermark with a low reliabilityscore.

Alternatively or in addition to the reliability score, a volume level ofthe captured audio signal can be analyzed to determine whether thewatermark has been detected in an audio signal near the metering device130 or far from the metering device 130 (e.g., is originating from themonitored media presentation device 120 near the metering device 130 oris originating from another device away from the metering device 130).

In such examples, upon determination of a low score, low volume, etc.,associated with a detected watermark, further analysis of the audiosignal can then proceed to block 806 as described below. Additionally,if no watermark is identified in the captured audio, then analysis movesto block 806 below.

At block 806, the example source detector 136 (FIG. 1) (and its adaptiveaudio filter 302, weight adjuster 304, and audio comparator 306 (FIG.3)) processes the captured audio signal to generate a comparison vector(e.g., such as the comparison vector 702 of the example of FIG. 7). Forexample, the echo cancellation algorithm of Equations 1-3 and theadaptive filter apparatus 600 are applied to the audio captured fromeach of the plurality of microphones 132, 134. As disclosed above withrespect to FIGS. 1-7, a vector of filter coefficients is generated bythe source detector 136 for comparison against the reference vector todetermine whether or not the captured audio is generated by the mediapresentation device 120 and/or by another ambient source such as theaudience members 140, 142.

At block 808, the example source detector 136 (FIG. 1) (and its audiocomparator 306 (FIG. 3)) compares the comparison vector for the capturedaudio signal to the reference vector to generate a similarity value. Forexample, a dot product is computed between the comparison vectorcoefficients and the reference vector coefficients to determine asimilarity or difference between the vectors. Such a similarity ordifference in the audio filter coefficients is indicative of a locationof origin for the audio signal, allowing the example source detector 136to determine whether a likely source for the audio signal 126 is or isnot the media presentation device 120, for example.

If the captured audio is determined to be emanating from the “known”location of the media presentation device 120, then the dot productcomparison yields a value of approximately 1.0, indicating a similaritybetween the vectors. At block 810, such a result processed by theexample state determiner 308 indicates that the media presentationdevice 120 is turned “on” and the audio matches the characteristics ofsound coming from the media presentation device 120 (e.g., audio 126from the television).

If the captured audio is determined to be ambient audio (e.g., emanatingfrom an audience member 140, 142 and/or other unidentified source suchas a device in an adjacent room, etc.), then the dot product comparisonyields a value of well below 0.5, indicating that the vectors aredissimilar. At block 812, such a result processed by the example statedeterminer 308 indicates that the media presentation device 120 isturned “off” (or has been muted or has the volume set too low to bedetectable by the microphones, etc.). In some examples, if the mediapresentation device 120 is determined to be “off”, then the creditor 139of the example metering device 130 discards, ignores, and/or marks asinvalid the captured audio signal and associated information (e.g.,timestamp, erroneous code, etc.).

At block 814, if the media presentation device 120 is determined to beon, then the example metering device 130 analyzes the captured audio.For example, the metering device 130 extracts a watermark from thecaptured audio signal. Alternatively or in addition, the examplemetering device 130 can process the audio signal to determine asignature for further analysis, for example. For example, the exampledecoder 138 decodes the captured audio signal to identify a watermarkembedded in the audio signal and/or process the captured audio signal tocompute a signature associated with the signal in the absence of awatermark. Identification of the watermark and processing of a signaturefrom the captured audio signal may occur in conjunction with the AME 160as well as with the decoder 138 and creditor 139 of the example meter130, for example.

At block 816, media exposure information is logged based on the signalanalysis (e.g., based on the identified watermark and/or computedsignature information). For example, based on the identification of theextracted watermark, the example creditor 139 captures an identificationof the media exposure based on the decoded watermark. The creditor 139may also assign a location identifier, timestamp, etc., to the decodeddata. The creditor 139 may transmit and/or otherwise work in conjunctionwith the AME 160 via the network 150, for example.

At block 818, results of the comparison of vectors at block 708 areevaluated to determine whether reference vector coefficients should berecalibrated. For example, changed conditions in the room 110 (e.g.,additional audience members 140, 142, moved furniture, repositioning ofthe media presentation device 120, etc.) can affect the coefficientsindicating the location of the media presentation device 120 and thedistinction between the media presentation device 120 and other ambientaudio sources (e.g., audience member 140, 142, etc.). In some examples,the example state determiner 308 may determine whether or not to triggera recalibration of the reference vector based on the availableinformation.

Alternatively or in addition to evaluating the vector comparison,recalibration of the reference coefficients can be triggered based onconfirmation of a valid watermark in the captured audio signal. Forexample, as described above, identification of a watermark in a capturedaudio signal and computation of a score associated with the watermark todetermine its reliability can validate that the media presentationdevice 120 is turned on and is outputting an audio signal 126 indicativeof media content exposed to the room 110. Thus, the operating state ofthe monitored media presentation device 120 and the validity of itsaudio output can be automatically determined and used to periodicallyverify or recalculate the reference vector of filter weightcoefficients. Such automated recalibration can be conductedcontinuously, after passage of a certain period of time, triggered basedon a consecutive number of dissimilar results, etc.

At block 820, if recalibration is triggered (e.g., based on the resultsof the comparison, based on an accurate extraction and identification ofwatermark data, triggered upon request, etc.), then weighted filtercoefficients for the reference vector are re-calculated using anadaptive echo cancellation algorithm such as the filter algorithm ofEquations 1-3 and the adaptive filter apparatus 600 of FIG. 6, which isapplied to an updated known audio sample (e.g., program audio generatedwhen the media presentation device 120 is known to be turned on)captured from each of the plurality of microphones 132, 134.

At block 822, state information is evaluated by the state determiner 308to determine whether monitoring is to continue. For example, stateinformation for the metering device 130 and/or instructions from the AME160 are evaluated by the example state determiner 308 to determinewhether to continue monitoring for audio from the media presentationdevice 120 in the room 110. If the example state determiner 308determines that monitoring is to continue, then control returns to block804 to monitor for a new audio signal. Otherwise, if the example statedeterminer 308 determines that monitoring is not to continue, then theexample process 800 ends.

FIG. 9 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example filterapparatus 600 of FIG. 6 and block 802 of the example process 800 of FIG.8 to process incoming audio to calculate filter taps or coefficients forcomparison to determine a state of the media presentation device 120(FIG. 1). While the example process of FIG. 9 as shown providesadditional detail regarding execution of block 802 of the exampleprocess 800 of FIG. 8 to provide a reference vector, the example processof FIG. 9 can also be applied to generate a comparison vector (block806) of comparison filter weight coefficients and/or to recalibratereference vector coefficients (block 820) for audio signal analysis, forexample.

At block 902, audio samples are received. For example, audio samples 602are received by the example microphone 132. At block 904, the receivedaudio samples are processed. For example, audio samples 602 received bythe microphone 132 are passed through a delay line 604 such that Msamples are shifted by a delay D. In the illustrated example, X_(M−1) isa most recent sample, and X₀ is an oldest sample. An output Y₀ is summedfrom samples as weighted by a reference set of coefficients {W_(m),m=0,1, . . . M−1} using Equation 1 applied to the filter apparatus 600.

At block 906, the processed audio samples from the first microphone arecompared to audio samples acquired from a second microphone. Forexample, the output Y₀ is subtracted from audio sample(s) X_(d) 606received by the example microphone 134. Thus, if the summed total of theweighted audio samples X_(M−1) to X₀ (as shifted using shift registersD) approximately matches the audio sample Xd, a difference between X_(d)and Y₀ is approximately zero.

At block 908, the difference between X_(d) and Y₀ is evaluated todetermine whether the difference is approximately zero or issignificantly greater than zero. At block 910, if the difference isapproximately zero, then weights in the reference vector may remainunchanged. If, however, the difference is greater than a threshold abovezero, then, at block 912, weights in the reference vector (e.g.,W_(M−1), W_(M−2), W_(M), . . . , W₀) are adjusted to new values based onEquations 2 and 3 disclosed above. For example, movement of the mediapresentation device 120, presence of audience members 140, 142, presenceof furniture, etc., in the room 110 may result in a change in soundcharacteristics that affects the weights in the reference vector andtrigger adjustment of reference vector weights.

At block 914, the reference vector is made available for use. Forexample, if weights were adjusted at block 912, the updated referencevector is made available for use in determining the state (e.g.,identity, on/off status, location, etc.) of the media presentationdevice 120 in the monitored room 110.

FIG. 10 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example sourcedetector 136 of FIGS. 1-3 and its components (e.g., the adaptive audiofilter of FIG. 6 and the state determiner of FIG. 7) to determine astate of the media presentation device 120 (FIG. 1). The example processof FIG. 10 provides additional and/or related detail regarding executionof block 808 of the example process 800 of FIG. 8.

Initially, at block 1002, a baseline set of weighted coefficients W_(m1)is received. For example, a reference vector, such as example referencevector 704, is received from the example weight adjuster 304 (FIG. 3),which generates the vector based on audio received by the first andsecond microphones 132, 134 (FIGS. 1-3) at a first time (e.g., usingEquations 1-3 as disclosed above in connection with FIG. 6). At block1004, a current set of weighted coefficients W_(m2) is received. Forexample, a comparison vector, such as the example comparison vector 702,is received from the weight adjuster 304, which generates the vectorbased on audio received by the first and second microphones 132, 134, ata second time after the first time.

At block 1006, the example audio comparator 306 calculates a dot productbetween W_(m1) and W_(m2). For example, as described above with respectto Equation 4 and FIG. 3, vectors W_(m1) and W_(m2) are converted tounit vectors and analyzed according to a dot product and/or othermathematical calculation. At block 1008, the example state determiner308 determines if the result of the dot product satisfies (e.g., isgreater than) a threshold.

If the result of the dot product satisfies the threshold (e.g., is closeto 1.0), then, at block 1010, the media presentation device 120 isdetermined to be “ON”. That is, based on the result of the dot product,the example state determiner 308 determines that the media presentationdevice 120 is ON. Based on the determination that the media presentationdevice 120 is ON, program control continues to block 810.

Otherwise, if the result of the dot product does not satisfy thethreshold (e.g., is close to zero), at block 1012, the mediapresentation device 120 is determined to be “OFF”. That is, based on theresult of the dot product, the example state determiner 308 determinesthat the media presentation device 120 is OFF. Based on thedetermination that the media presentation device 120 is OFF (or isotherwise not generating detectable audio), program control continues toblock 812.

FIG. 11 is a block diagram of an example processor platform 1100 capableof executing the instructions of FIGS. 8-10 to implement the examplesource detector 136 (and its components) of FIGS. 1-3 and 6-7. Theprocessor platform 1100 can be, for example, a server, a personalcomputer, a mobile device (e.g., a cell phone, a smart phone, a tabletsuch as an iPad™), a personal digital assistant (PDA), an Internetappliance, a DVD player, a CD player, a digital video recorder, aBlu-ray player, a gaming console, a personal video recorder, a set topbox, or any other type of computing device.

The processor platform 1100 of the illustrated example includes aprocessor 1112. The processor 1112 of the illustrated example ishardware. For example, the processor 1112 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer. In the illustrated example, theprocessor 1112 is structures to include the example adaptive audiofilter 302, the example weight adjuster 304, the example audiocomparator 306, and the example state determiner 308 of the examplesource detector 136.

The processor 1112 of the illustrated example includes a local memory1113 (e.g., a cache). The processor 1112 of the illustrated example isin communication with a main memory including a volatile memory 1114 anda non-volatile memory 1116 via a bus 1118. The volatile memory 1114 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory(RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1116 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1114,1116 is controlled by a memory controller.

The processor platform 1100 of the illustrated example also includes aninterface circuit 1120. The interface circuit 1120 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1122 are connectedto the interface circuit 1120. The input device(s) 1122 permit(s) a userto enter data and commands into the processor 1112. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1124 are also connected to the interfacecircuit 1120 of the illustrated example. The output devices 1124 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1120 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1120 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1126 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1100 of the illustrated example also includes oneor more mass storage devices 1128 for storing software and/or data.Examples of such mass storage devices 1128 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 1132 representing the flow diagrams of FIGS. 8-10 maybe stored in the mass storage device 1128, in the volatile memory 1114,in the non-volatile memory 1116, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that examples have beendisclosed which allow a meter 130 (FIG. 1) to distinguish betweenmeasurable content and ambient sound to determine an operating state ofa media presentation device and improve accuracy of audiencemeasurement. Because the meter 130 can automatically and autonomouslymonitor, analyze, and determine operating state and validity ofmonitored data, additional devices, panelist involve, and externaloversight can be avoided, resulting in increased accuracy and robustnessas well as convenience.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus to determine a source of an audiosignal captured by a microphone, the apparatus comprising: memory tostore instructions; and at least one processor to execute theinstructions to at least: calibrate a reference vector of coefficientsfor audio monitoring using a known audio sample from a location; comparea comparison vector of coefficients for the audio signal captured at thelocation to the reference vector of coefficients to generate asimilarity value; evaluate the similarity value and the comparing of thecomparison vector of coefficients to the reference vector ofcoefficients to determine reliability of the reference vector ofcoefficients; and when the evaluation determines that the referencevector of coefficients is unreliable, trigger recalibration of thereference vector of coefficients using an updated known audio sample. 2.The apparatus of claim 1, wherein the coefficients of the referencevector are filter coefficients generated from the known audio samplecaptured using a plurality of microphones.
 3. The apparatus of claim 1,wherein the coefficients of the reference vector are weighted.
 4. Theapparatus of claim 1, wherein the coefficients of the comparison vectorare filter coefficients generated from the captured audio signalcaptured using a plurality of microphones.
 5. The apparatus of claim 4,wherein the coefficients of the reference vector are weighted.
 6. Theapparatus of claim 1, wherein the comparing of the comparison vector ofcoefficients and the reference vector of coefficients includes computinga dot product between the comparison vector of coefficients and thereference vector of coefficients to generate the similarity value. 7.The apparatus of claim 1, wherein the comparison vector of coefficientsis generated from the captured audio signal using echo cancellation andan adaptive filter.
 8. The apparatus of claim 1, wherein recalibrationof the reference vector of coefficients is to be triggered byidentification of a watermark in the captured audio signal.
 9. Theapparatus of claim 1, wherein the reference vector of coefficients isdetermined to be unreliable based on at least one of a period of time ora number of dissimilar results indicated by the similarity value below athreshold.
 10. A non-transitory computer readable storage mediumcomprising instructions that, when executed, cause at least oneprocessor to at least: calibrate a reference vector of coefficients foraudio monitoring using a known audio sample from a location; compare acomparison vector of coefficients for an audio signal captured at thelocation to the reference vector of coefficients to generate asimilarity value; evaluate the similarity value and the comparing of thecomparison vector of coefficients to the reference vector ofcoefficients to determine reliability of the reference vector ofcoefficients; and when the evaluation determines that the referencevector of coefficients is unreliable, trigger recalibration of thereference vector of coefficients using an updated known audio sample.11. The non-transitory computer readable storage medium of claim 10,wherein the instructions, when executed, cause the at least oneprocessor to generate the coefficients of the reference vector as filtercoefficients from the known audio sample captured using a plurality ofmicrophones.
 12. The non-transitory computer readable storage medium ofclaim 10, wherein the instructions, when executed, cause the at leastone processor to weight the coefficients of the reference vector. 13.The non-transitory computer readable storage medium of claim 10, whereinthe instructions, when executed, cause the at least one processor togenerate the coefficients of the comparison vector as filtercoefficients from the audio signal captured at the location using aplurality of microphones.
 14. The non-transitory computer readablestorage medium of claim 13, wherein the instructions, when executed,cause the at least one processor to weight the coefficients of thereference vector.
 15. The non-transitory computer readable storagemedium of claim 10, wherein the instructions, when executed, cause theat least one processor to compare the comparison vector of coefficientsand the reference vector of coefficients by computing a dot productbetween the comparison vector of coefficients and the reference vectorof coefficients to generate the similarity value.
 16. The non-transitorycomputer readable storage medium of claim 10, wherein the instructions,when executed, cause the at least one processor to generate thecomparison vector of coefficients from the captured audio signal usingecho cancellation and an adaptive filter.
 17. The non-transitorycomputer readable storage medium of claim 10, wherein the instructions,when executed, cause the at least one processor to trigger recalibrationof the reference vector of coefficients by identification of a watermarkin the captured audio signal.
 18. The non-transitory computer readablestorage medium of claim 10, wherein the instructions, when executed,cause the at least one processor to determine that the reference vectorof coefficients is unreliable based on at least one of a period of timeor a number of dissimilar results indicated by the similarity valuebelow a threshold.
 19. A method to determine a source of an audio signalcaptured by a microphone, the method comprising: calibrating a referencevector of coefficients for audio monitoring using a known audio samplefrom a location; comparing a comparison vector of coefficients for anaudio signal captured at the location to the reference vector ofcoefficients to generate a similarity value; evaluating the similarityvalue and the comparing of the comparison vector of coefficients to thereference vector of coefficients to determine reliability of thereference vector of coefficients; and when the evaluation determinesthat the reference vector of coefficients is unreliable, triggeringrecalibration of the reference vector of coefficients using an updatedknown audio sample.
 20. The method of claim 19, wherein comparing thecomparison vector of coefficients and the reference vector ofcoefficients includes computing a dot product between the comparisonvector of coefficients and the reference vector of coefficients togenerate the similarity value.
 21. The method of claim 19 whereintriggering recalibration of the reference vector of coefficientsincludes triggering by identification of a watermark in the capturedaudio signal.
 22. The method of claim 19, further including generatingthe comparison vector of coefficients from the captured audio signalusing echo cancellation and an adaptive filter.